Predicted temperature and precipitation values were generated throughout the state of Massachusetts using a stochastic weather generator (SWG) model to develop various climate change scenarios (Steinschneider and Najibi, 2022a). This data release contains temperature and precipitation statistics (SWG_outputTable.csv) derived from the SWG model under the surface warming derived from the RCP 8.5 climate change emissions scenario at 30-year moving averages centered around 2030, 2050, 2070, 2090. During the climate modeling process, extreme precipitation values were also generated by scaling previously published intensity-duration-frequency (IDF) values from the NOAA Atlas 14 database (Perica and others, 2015) by a factor per degree expected warming produced from the SWG model generator (Najibi and others, 2022; Steinschneider and Najibi, 2022b, c). These newly generated IDF values (IDF_outputTable.csv) account for expected changes in extreme precipitation driven by variations in weather associated with climate change throughout the state of Massachusetts. The data presented here were developed in collaboration with the Massachusetts Executive Office of Energy and Environmental Affairs and housed on the Massachusetts climate change clearinghouse webpage (Massachusetts Executive Office of Energy and Environmental Affairs, 2022). References: Massachusetts Executive Office of Energy and Environmental Affairs, 2022, Resilient MA Maps and Data Center at URL https://resilientma-mapcenter-mass-eoeea.hub.arcgis.com/ Najibi, N., Mukhopadhyay, S., and Steinschneider, S., 2022, Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity: Geophysical Research Letters, v. 49, no. 8, 14 p., https://doi.org/10.1029/2021GL097100. Perica, S., Pavlovic, S., St. Laurent, M., Trypaluk, C., Unruh, D., Martin, D., and Wilhite, O., 2015, NOAA Atlas 14 Volume 10 Version 3, Precipitation-Frequency Atlas of the United States, Northeastern States (revised 2019): NOAA, National Weather Service, https://doi.org/10.25923/99jt-a543. Steinschneider, S., and Najibi, N., 2022a, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf. Steinschneider, S., and Najibi, N., 2022b, Future projections of extreme precipitation across Massachusetts—a theory-based approach: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_IDF_Curves_Dec2021.pdf. Steinschneider, S., and Najibi, N., 2022c, Observed and projected scaling of daily extreme precipitation with dew point temperature at annual and seasonal scales across the northeast United States: Journal of Hydrometeorology, v. 23, no. 3, p. 403-419, https://doi.org/10.1175/JHM-D-21-0183.1.

Led by the Massachusetts Executive Office of Energy and Environmental Affairs (EEA), in partnership with Cornell University, U.S. Geological Survey and Tufts University, the Massachusetts Climate and Hydrologic Risk Project (Phase 1) has developed new climate change projections for the Commonwealth. These new temperature and precipitation projections are downscaled for Massachusetts at the HUC8 watershed scale using Global Climate Models (GCMs) and a Stochastic Weather Generator (SWG) developed by Cornell University.

Stochastic weather generators provide a computationally efficient and complementary alternative to direct use of GCMs for investigating water system performance under climate stress. These models are configured based on existing meteorological records (i.e., historical weather) and are then used to generate large ensembles of simulated daily weather records that are similar to but not bound by variability in past observations. Once fit to historical data, model parameters can be systematically altered to produce new traces of weather that exhibit a wide range of change in their distributional characteristics, including the intensity and frequency of average and extreme precipitation, heatwaves, and cold spells.

The Phase 1 SWG was developed, calibrated, and validated across all HUC8 watersheds that intersect with the state of Massachusetts. A set of climate change scenarios for those watersheds were generated that only reflect mechanisms of thermodynamic climate change deemed to be most credible. These thermodynamic climate changes are based on the range of temperature projections produced by a set of downscaled GCMs for the region. The temperature and precipitation projections presented in this dashboard reflect a warming scenario linked to the Representation Concentration Pathway (RCP) 8.5, a comparatively high greenhouse gas emissions scenario.

The statistics presented in this series of map layers are expressed as either a percent change or absolute change (see list of layers with units and definitions below). These changes are referenced to baseline values that are calculated based on the median value across the 50 model ensemble members associated with the 0°C temperature change scenario derived from observational data (1950-2013) from Livneh et al. (2015). The temperature projections derived from the downscaled GCMs for the region, which are used to drive the SGW, are averaged across 30 years and centered on a target decade (i.e., 2030, 2050, 2070). Projections for 2090 are averaged across 20 years.Definitions of climate projection metrics (with units of change):Total Precipitation (% change): The average total precipitation within a calendar year. Maximum Precipitation (% change): The maximum daily precipitation in the entire record. Precipitation Depth – 90th Percentile Storm (% change): The 90th percentile of non-zero precipitation. Precipitation Depth –99th Percentile Storm (% change): The 99th percentile of non-zero precipitation. Consecutive Wet Days (# days): The average number of days that exist within a run of 2 or more wet days. Consecutive Dry Days (# days): The average number of days that exist within a model run of 2 or more dry days. Days above 1 inch (# days): The number of days with precipitation greater than 1 inch. Days above 2 inches (# days): The number of days with precipitation greater than 2 inches.Days above 4 inches (# days): The number of days with precipitation greater than 4 inches.Maximum Temperature (°F): The maximum daily average temperature value in the entire recordAverage Temperature (°F): Daily average temperature.Days below 0 °F (# days): The number of days with temperature below 0 °F.Days below 32 °F (# days): The number of days with temperature below 32 °F.Maximum Duration of Coldwaves (# days): Longest duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Average Duration of Coldwaves (# days): Average duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Number of Coldwave Events (# events): Number of instances with ten or more consecutive days with temperature below 20 °F.Number of Coldstress Events (# events): Number of instances when a 3-day moving average of temperature is less than 32 °F. Days above 100 °F (# days): The number of days with temperature above 100 °F.Days above 95 °F (# days): The number of days with temperature above 95 °F.Days above 90 °F (# days): The number of days with temperature above 90 °F.Maximum Duration of Heatwaves (# days): Longest duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Average Duration of Heatwaves (# days): Average duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Number of Heatwave Events (# events): Number of instances with three or more consecutive days with temperature over 90 °F.Number of Heatstress Events (# events): Number of instances when a 3-day moving average of temperature is above 86 °F.Cooling Degree Days (# degree-day): Cooling degree days assume that when the outside temperature is below 65°F, we don't need cooling (air-conditioning) to be comfortable. Cooling degree-days are the difference between the daily temperature mean and 65°F. For example, if the temperature mean is 85°F, we subtract 65 from the mean and the result is 20 cooling degree-days for that day. (Definition adapted from National Weather Service).Heating Degree Days (# degree-day): Heating degree-days assume that when the outside temperature is above 65°F, we don't need heating to be comfortable. Heating degree days are the difference between the daily temperature mean and 65°F. For example, if the mean temperature mean is 25°F, we subtract the mean from 65 and the result is 40 heating degree-days for that day. (Definition adapted from National Weather Service).Growing Degree Days (# degree-day): A growing degree day (GDD) is an index used to express crop maturity. The index is computed by subtracting a base temperature of 50°F from the average of the maximum and minimum temperatures for the day. Minimum temperatures less than 50°F are set to 50, and maximum temperatures greater than 86°F are set to 86. These substitutions indicate that no appreciable growth is detected with temperatures lower than 50° or greater than 86°. (Adapted from National Weather Service).Please see additional information related to this project and dataset in the Climate Change Projection Dashboard on the Resilient MA Maps and Data Center webpage.

This excel contains data for Chapter 2 “Precipitation” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual precipitation at Providence, RI,” (page 64); Figure 2, “Annual precipitation at Worcester, MA,” (page 64); Figure 3, “Annual Palmer Drought Severity Index (PDSI) for Rhode Island,” (page 65); Figure 4, "Annual Palmer Drought Severity Index (PDSI) for Massachusetts," (page 65); Figure 5, "Climate model projection of winter total precipitation in RI or MA to 2100," (page 67); and Figure 6, "Climate model projection of winter annual snowfall in RI or MA to 2100," (page 67). For more information, please reference the Technical Report or contact info@nbep.org. Original figures are available at http://nbep.org/the-state-of-our-watershed/figures/.

Year 2013 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The Woods Hole Oceanographic Institution has built the Martha's Vineyard Coastal Observatory (MVCO) near South Beach in Edgartown, Massachusetts. The project was initiated by scientists in the Coastal and Ocean Fluid Dynamics Laboratory (COFDL) at WHOI, who will use the observatory to study coastal atmospheric and oceanic processes.

     Data from the observatory are downloaded from the shore lab every
     twenty minutes: 5, 25 & 45 minutes after the hour. They are processed
     to provide burst averaged statistics, with the most current data
     presented on the MVCO home page. Summary files of the meteorological
     and oceanographic data are provided in the MetDat_s files. Historical
     burst averaged data can be retrieved via a web interface (JGOFS
     format) or anonymous ftp (mvcodata.whoi.edu).

     The time (Yday) is GMT (UTM) at the start of the burst, where 1.5
 represents
     noon on January 1.

     Anonymous ftp ("ftp://mvcodata.whoi.edu/pub/mvcodata/data/") access
 provides
     ascii flat files including year-to-date data in files named
 YYYY_InstID.CNN
     (eg., 2002_Campmt.C03) or data from each day as YYYYyday_InstID.CNN
 (eg.,
     2002122_Campmt.C03). The Yday 1.5 represents noon on January 1. Data are
     available to download. The user must navigate to the desired
 Instrument_ID
     (eg., Campmt_s) and the desired year (eg., 2000). Text files and header
 files
     define the contents of these data files. The year-to-date files can be
 read
     using one of the example Matlab m-files.

     The web-interface ("http://mvcodata.whoi.edu/jg/dir/mvco/") provides
 methods to
     subset the data. Retrieved ascii data will contain a header at the top.
 The
     web-interface also provides interactive graphics (Simple X-Y plotting).

AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.

How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.

Data Format: GeoTIFF

Spatial Coverage: 60°E–150°E, 15°S–55°N, land.

AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at https://doi.org/10.5281/zenodo.6367463

AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at https://doi.org/10.5281/zenodo.6369796

AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at https://doi.org/10.5281/zenodo.4266081

Year 2003 meteorological measurements at Governor's Academy of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

Year 2007 meteorological measurements at Governor's Academy and MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

Tropical biodiversity overshadows the number of species inhabiting other regions. Age, area, and stability constitute three classical ideas used to explain the higher richness in these warm and humid zones. In this study, we measured the global dynamics of tropical, arid, temperate, cold, and polar climate zones over the last 5 million years (Ma). We aimed to evaluate whether the age, area, and stability of these climate zones contribute to explaining the observed differences in species richness. We classified the paleoclimatic layers generated by the PALEO-PGEM climatic emulator – temperature and precipitation for the last 5 Ma at 1,000-year intervals – into the main Köppen-Geiger climate zones: tropical, arid, temperate, cold, and polar. We then calculated three variables: age, area, and stability. Age represents the duration that each map cell has remained within its current climate zone since its last change (map cell-based measure). Area quantifies the total extent of each climate ..., Climate data We focused on the last 5 Ma, encompassing the Pliocene (5.3-2.6 Ma), Pleistocene (2.6-0.01 Ma), and Holocene (0.01 Ma ago to the present day; Cohen et al., 2021). This period started with a subtle warming trend in the early Pliocene (until 3.2 Ma), continuing with successive cooling pulses that culminated with the establishment of continental northern hemisphere glaciations (Zachos et al., 2001). Moreover, orographic and tectonic events occurred during this period, such as the gradual uplift of the Andes (Gregory-Wodzicki, 2000). To examine how these climatic changes influence biogeographic patterns, we used the high-resolution climate emulator PALEO-PGEM (Holden et al., 2019), which provides global temperature and rainfall monthly data with 1,000-year temporal resolution and 0.5º spatial resolution (Fig. 1). Using monthly mean temperature (°C) and total rainfall (mm) data from PALEO-PGEM, we created 10,002 arrays representing each time step over the past 5 Ma (two arrays o..., , # Data for: Classic hypotheses of area, time, and climatic stability fall short in explaining high tropical species richness

https://doi.org/10.5061/dryad.h70rxwdv4

Description of the data and file structure

Code and data files for running the analyses of the article "Classic Hypotheses of Area, Time, and Climatic Stability Fall Short in Explaining High Tropical Species Richness". This repository includes area and richness data, climate classification arrays, landmass shapefiles, species presence/absence databases, and the R script used for processing and analysing.

Files and variables

File: Area_databases.zip

Description:Â

• "Total_area_global.csv": Global area (km²) of climate zones across all time steps.
  • "class": climate zone (1 = Tropical; 2 =Â Arid; 3 = Temperate; 4 = Cold; 5 = Polar).
  • "value": area in square kilometres (km2).
  • "layer": Time step (from present as "Time 1", to 5 million years as "Time 5,00...,